我正在尝试将Series系列连接到列名为“ RSI”的数据框的右侧。但是，由于Series的长度比数据框中的其他列短，因此我需要确保将NaN值附加到该列的顶部而不是底部。现在，我已经使用了以下代码，但找不到能使我获得所需输出的参数。
RSI = pd.Series(RSI)
df = pd.concat((df, RSI.rename('RSI'), axis='columns')
到目前为止，这是我的输出：
Dates Prices Volumes RSI
0 2013-02-08 201.68 2893254 47.7357
1 2013-02-11 200.16 2944651 53.3967
2 2013-02-12 200.04 2461779 56.3866
3 2013-02-13 200.09 2169757 60.1845
4 2013-02-14 199.65 3294126 62.1784
5 2013-02-15 200.98 3627887 63.9720
6 2013-02-19 200.32 2998317 62.9671
7 2013-02-20 199.31 3715311 63.9232
8 2013-02-21 198.33 3923051 66.8817
9 2013-02-22 201.09 3107876 72.8258
10 2013-02-25 197.51 3845276 69.6578
11 2013-02-26 199.14 3391562 63.8458
12 2013-02-27 202.33 4185545 64.2776
13 2013-02-28 200.83 4689698 67.2445
14 2013-03-01 202.91 3308544 58.2408
15 2013-03-04 205.19 3693365 57.7058
16 2013-03-05 206.53 3807706 53.7482
17 2013-03-06 208.38 3594899 57.5396
18 2013-03-07 209.42 3884317 53.2722
19 2013-03-08 210.38 3700086 58.6824
20 2013-03-11 210.08 3048901 56.0161
21 2013-03-12 210.55 3591261 60.2066
22 2013-03-13 212.06 3355969 55.3322
23 2013-03-14 215.80 5505484 51.7492
24 2013-03-15 214.92 7935024 47.1241
25 2013-03-18 213.21 3006125 46.9102
26 2013-03-19 213.44 3198577 46.6569
27 2013-03-20 215.06 3019153 54.0822
28 2013-03-21 212.26 5830566 56.2525
29 2013-03-22 212.08 3015847 51.8359
... ... ... ... ...
1229 2017-12-26 152.83 2479017 80.1930
1230 2017-12-27 153.13 2149257 80.7444
1231 2017-12-28 154.04 2687624 56.4425
1232 2017-12-29 153.42 3327087 56.9183
1233 2018-01-02 154.25 4202503 63.6958
1234 2018-01-03 158.49 9441567 61.1962
1235 2018-01-04 161.70 7556249 61.3816
1236 2018-01-05 162.49 5195764 64.7724
1237 2018-01-08 163.47 5237523 63.0508
1238 2018-01-09 163.83 4341810 53.9559
1239 2018-01-10 164.18 4174105 54.1351
1240 2018-01-11 164.20 3794453 50.6824
1241 2018-01-12 163.14 5031886 43.0222
1242 2018-01-16 163.85 7794195 32.7428
1243 2018-01-17 168.65 11710033 39.4754
1244 2018-01-18 169.12 14259345 37.3409
1245 2018-01-19 162.37 21172488 NaN
1246 2018-01-22 162.60 8480795 NaN
1247 2018-01-23 166.25 7466232 NaN
1248 2018-01-24 165.37 5645003 NaN
1249 2018-01-25 165.47 3302520 NaN
1250 2018-01-26 167.34 3787913 NaN
1251 2018-01-29 166.80 3516995 NaN
1252 2018-01-30 163.62 4902341 NaN
1253 2018-01-31 163.70 4072830 NaN
1254 2018-02-01 162.40 4434242 NaN
1255 2018-02-02 159.03 5251938 NaN
1256 2018-02-05 152.53 8746599 NaN
1257 2018-02-06 155.34 9867678 NaN
1258 2018-02-07 153.85 6149207 NaN
但是，我需要它看起来像这样：
Dates Prices Volumes RSI
0 2013-02-08 201.68 2893254 NaN
1 2013-02-11 200.16 2944651 NaN
2 2013-02-12 200.04 2461779 NaN
3 2013-02-13 200.09 2169757 NaN
4 2013-02-14 199.65 3294126 NaN
5 2013-02-15 200.98 3627887 NaN
6 2013-02-19 200.32 2998317 NaN
7 2013-02-20 199.31 3715311 NaN
8 2013-02-21 198.33 3923051 NaN
9 2013-02-22 201.09 3107876 NaN
10 2013-02-25 197.51 3845276 NaN
11 2013-02-26 199.14 3391562 NaN
12 2013-02-27 202.33 4185545 NaN
13 2013-02-28 200.83 4689698 NaN
14 2013-03-01 202.91 3308544 NaN
15 2013-03-04 205.19 3693365 57.7058
16 2013-03-05 206.53 3807706 53.7482
17 2013-03-06 208.38 3594899 57.5396
18 2013-03-07 209.42 3884317 53.2722
19 2013-03-08 210.38 3700086 58.6824
20 2013-03-11 210.08 3048901 56.0161
21 2013-03-12 210.55 3591261 60.2066
22 2013-03-13 212.06 3355969 55.3322
23 2013-03-14 215.80 5505484 51.7492
24 2013-03-15 214.92 7935024 47.1241
25 2013-03-18 213.21 3006125 46.9102
26 2013-03-19 213.44 3198577 46.6569
27 2013-03-20 215.06 3019153 54.0822
28 2013-03-21 212.26 5830566 56.2525
29 2013-03-22 212.08 3015847 51.8359
... ... ... ... ...
1229 2017-12-26 152.83 2479017 80.1930
1230 2017-12-27 153.13 2149257 80.7444
1231 2017-12-28 154.04 2687624 56.4425
1232 2017-12-29 153.42 3327087 56.9183
1233 2018-01-02 154.25 4202503 63.6958
1234 2018-01-03 158.49 9441567 61.1962
1235 2018-01-04 161.70 7556249 61.3816
1236 2018-01-05 162.49 5195764 64.7724
1237 2018-01-08 163.47 5237523 63.0508
1238 2018-01-09 163.83 4341810 53.9559
1239 2018-01-10 164.18 4174105 54.1351
1240 2018-01-11 164.20 3794453 50.6824
1241 2018-01-12 163.14 5031886 43.0222
1242 2018-01-16 163.85 7794195 32.7428
1243 2018-01-17 168.65 11710033 39.4754
1244 2018-01-18 169.12 14259345 36.9999
1245 2018-01-19 162.37 21172488 41.1297
1246 2018-01-22 162.60 8480795 12.1231
1247 2018-01-23 166.25 7466232 39.0977
1248 2018-01-24 165.37 5645003 63.6958
1249 2018-01-25 165.47 3302520 56.4425
1250 2018-01-26 167.34 3787913 80.7444
1251 2018-01-29 166.80 3516995 61.1962
1252 2018-01-30 163.62 4902341 58.6824
1253 2018-01-31 163.70 4072830 53.7482
1254 2018-02-01 162.40 4434242 43.0222
1255 2018-02-02 159.03 5251938 61.1962
1256 2018-02-05 152.53 8746599 56.4425
1257 2018-02-06 155.34 9867678 36.0978
1258 2018-02-07 153.85 6149207 41.1311
感谢您的帮助。
答案 0 :(得分：0)
尝试这样：
df["RSI"].shift(len(df)-len(df["RSI"].dropna()))
答案 1 :(得分：0)
另一种方法是操纵rsi系列索引以自底向上匹配df索引（我仅将示例的13行用于演示）
size_diff = df.index.size - rsi.index.size
rsi.index = df.index[size_diff:]
pd.concat([df, rsi], axis=1)
Out[1490]:
Dates Prices Volumes RSI
0 2013-02-08 201.68 2893254 NaN
1 2013-02-11 200.16 2944651 NaN
2 2013-02-12 200.04 2461779 NaN
3 2013-02-13 200.09 2169757 NaN
4 2013-02-14 199.65 3294126 NaN
5 2013-02-15 200.98 3627887 47.7357
6 2013-02-19 200.32 2998317 53.3967
7 2013-02-20 199.31 3715311 56.3866
8 2013-02-21 198.33 3923051 60.1845
9 2013-02-22 201.09 3107876 62.1784
10 2013-02-25 197.51 3845276 63.9720
11 2013-02-26 199.14 3391562 62.9671
12 2013-02-27 202.33 4185545 63.9232
13 2013-02-28 200.83 4689698 66.8817
答案 2 :(得分：0)
我们可以得到Series和dataframe之间的行数差异。
然后将NaN中的差异附加到np.repeat的系列中（顶部）
最后将带有NaN的新系列附加到原始的数据框（沿axis = 1（列））
diff = df.shape[0] - RSI.shape[0]
rpts = np.repeat(np.NaN, diff)
RSI = pd.concat([pd.Series(rpts, name='RSI'), RSI], ignore_index=True)
pd.concat([df, RSI['RSI']], axis=1).head(20)
Dates Prices Volumes RSI
0 2013-02-08 201.68 2893254 NaN
1 2013-02-11 200.16 2944651 NaN
2 2013-02-12 200.04 2461779 NaN
3 2013-02-13 200.09 2169757 NaN
4 2013-02-14 199.65 3294126 NaN
5 2013-02-15 200.98 3627887 NaN
6 2013-02-19 200.32 2998317 NaN
7 2013-02-20 199.31 3715311 NaN
8 2013-02-21 198.33 3923051 NaN
9 2013-02-22 201.09 3107876 NaN
10 2013-02-25 197.51 3845276 NaN
11 2013-02-26 199.14 3391562 NaN
12 2013-02-27 202.33 4185545 NaN
13 2013-02-28 200.83 4689698 47.7357
14 2013-03-01 202.91 3308544 53.3967
15 2013-03-04 205.19 3693365 56.3866
16 2013-03-05 206.53 3807706 60.1845
17 2013-03-06 208.38 3594899 62.1784
18 2013-03-07 209.42 3884317 63.9720
19 2013-03-08 210.38 3700086 62.9671